Trends prediction of monthly gout incidence and comparison of model performance among four time series models

Hu Min; Du Chengfeng; Tang Xiaojun; Deng Dan

doi:10.13406/j.cnki.cyxb.001308

您当前的位置：

首页 >

文章列表页 >

Trends prediction of monthly gout incidence and comparison of model performance among four time series models

更新时间：2024-01-17

- Trends prediction of monthly gout incidence and comparison of model performance among four time series models
- Journal of Chongqing Medical University Vol. 42, Issue 5, Pages: 537-541(2017)
- 作者机构：
  
  重庆医科大学公共卫生与管理学院卫生统计与信息管理教研室
- 作者简介：
- 基金信息：
- DOI：10.13406/j.cnki.cyxb.001308
  CLC： R589.7
- Published：2017，
扫描看全文
Hu Min, Du Chengfeng, Tang Xiaojun, et al. Trends prediction of monthly gout incidence and comparison of model performance among four time series models. [J]. Journal of Chongqing Medical University 42(5):537-541(2017)
DOI：

Hu Min, Du Chengfeng, Tang Xiaojun, et al. Trends prediction of monthly gout incidence and comparison of model performance among four time series models. [J]. Journal of Chongqing Medical University 42(5):537-541(2017) DOI： 10.13406/j.cnki.cyxb.001308.

摘要

目的:选择最优时间序列模型进行重庆市痛风月发病趋势预测。方法:回顾性收集重庆市某三甲医院2011年1月至2014年12月痛风月发病数据

并采用指数平滑法、ARIMA(p

q)模型、GM(1

1)模型和Markov模型对其发病趋势进行拟合

根据平均百分绝对误差的大小判断模型优劣

选择最优时间序列模型进行痛风月发病趋势预测。结果:2012年9月后痛风月发病人数激增

并呈现一定的周期性。但就整个观察期而言

序列波动明显

周期性不明显。4种时间序列模型分析结果显示:实际发病数在各模型预测值95%的可信范围内波动

但不同模型预测结果均存在一定程度偏离。通过计算可知:ARIMA(1

0)、GM(1

1)和指数平滑法的平均百分绝对误差分别为24.72、109.21和24.95。因此GM(1

1)模型预测效果偏离最大

指数平滑法次之

ARIMA(1

0)模型拟合效果最贴近实际。结论:GM(1

1)模型拟合效果最差

ARIMA(1

0)模型拟合效果最好

建议用ARIMA(1

0)模型进行未来痛风发病预测。

Abstract

Objective:To choose the optimal time series model for monthly gout incidence prediction. Methods:Monthly gout incidence data were collected retrospectively from an affiliated hospital of Chongqing Medical University from January 2011 to December 2014.And exponential smoothing method

ARIMA（p

q）

GM（1

1）and Markov model were adopted to fit the trend of monthly gout incidence

respectively. The efficiency comparison between different models was judged by mean absolute percentage error

and model with least mean absolute percentage error was used for future prediction. Results:Monthly gout incidence increased greatly

and cyclicality was observed after September 2012. But only sequence fluctuation was observed on the whole observation period. Results of trend prediction among four time series models were in consistent with each other

but their efficacy were different. The maximum and minimum mean absolute percentage error was in GM（1

1）model and ARIMA（1

0）model

respectively. Thus fitting results of ARIMA（1

0）model was mostly close to the actual situation. Conclusion:The ARIMA（1

0）model have the best imitative effect.Hence

ARIMA（1

0）model should be used for future monthly gout incidence prediction.

关键词

痛风时间序列分析趋势预测模型选择

Keywords

GoutTime series analysistrends predictionmodel selection

references

Views

143

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

MIS-DIAGNOSES ABOUT GOUTY ARTHRITIS───AN ANALYSIS OF TWENTY-EIGHT CASES

The application study of time series analysis in breast cancer patients’ hospitalized costs

Related Author

An Hong

et al

DENG Dan

et al

周燕荣

Related Institution

Department of Health Statistics and Information Management,College of Public Health,Chongqing Medical University

重庆医科大学公共卫生学院卫生统计与信息管理教研室

⁰